Learning Combinatorial Interaction Test Generation Strategies Using Hyperheuristic Search

The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left...

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Vydáno v:2015 IEEE/ACM 37th IEEE International Conference on Software Engineering Ročník 1; s. 540 - 550
Hlavní autoři: Yue Jia, Cohen, Myra B., Harman, Mark, Petke, Justyna
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2015
Témata:
Algorithm design and analysis
Arrays
CIT
Heuristic algorithms
Hyperheuristic
SBSE
Search problems
Simulated annealing
Software algorithms
Testing
ISSN:0270-5257
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Shrnutí:The surge of search based software engineering research has been hampered by the need to develop customized search algorithms for different classes of the same problem. For instance, two decades of bespoke Combinatorial Interaction Testing (CIT) algorithm development, our exemplar problem, has left software engineers with a bewildering choice of CIT techniques, each specialized for a particular task. This paper proposes the use of a single hyperheuristic algorithm that learns search strategies across a broad range of problem instances, providing a single generalist approach. We have developed a Hyperheuristic algorithm for CIT, and report experiments that show that our algorithm competes with known best solutions across constrained and unconstrained problems: For all 26 real-world subjects, it equals or outperforms the best result previously reported in the literature. We also present evidence that our algorithm's strong generic performance results from its unsupervised learning. Hyperheuristic search is thus a promising way to relocate CIT design intelligence from human to machine.
ISSN:0270-5257
DOI:10.1109/ICSE.2015.71